The Tool-Builder Assumption
The AI democratization story assumes that better tools produce better workflows. Most professionals aren't looking for automation opportunities — they're focused on their domain. The bottleneck isn't access. It's knowing which problem to solve.
- ai strategy
- enterprise ai
- workflow
- adoption
Benedict Evans has been one of the more reliable readers of how technology moves through markets for the better part of two decades. His newsletter is one of the few places where you get the unexcited version, no hype, just the structural pattern. His recent piece on AI tools and enterprise transformation identifies something the mainstream AI conversation keeps getting wrong.
The Silicon Valley assumption about AI, Evans argues, is essentially an engineer's assumption: that giving people better tools means they'll naturally build better processes. It misreads how most professionals think about their work.
"Most people are not tool builders," Evans writes, "and most people don't instinctively think about how their job could be done in a different way." A lawyer is focused on the law. A salesperson is focused on the client. Neither is spending their downtime mapping their workflow and asking what could be automated. The reason isn't laziness or lack of curiosity. Domain expertise and process redesign are genuinely different skills, and most professionals have the first one and not the second.
This is an old pattern. Evans draws the parallel directly: giving every company a PC and Lotus 123 in the 1980s didn't automatically transform invoice processing. Putting browsers in front of every employee in the 1990s didn't rebuild supply chains. Access to the tool didn't produce the transformation. The transformation required someone asking a different question about how the work should be structured, and then redesigning around the answer.
AI access is the current version of this. Most organizations now have it. The bottleneck has moved.
Evans's sharpest observation is about where the real difficulty sits: "The hard part is knowing that you need a tool for this in the first place." Not building it. Not deploying it. Recognizing that the problem exists, that it's worth solving, and that it can be reframed in a way that's actually automatable. That requires stepping outside the work long enough to see how it's structured. Most people doing the work don't have that vantage point, not because they're not capable, but because being good at a job and being able to redesign it are different things.
Historical automation success, Evans notes, typically required multiple failed attempts before finding the right framing. The teams that eventually built something useful often weren't the domain experts. They were people who could redefine what the problem was.
For organizations trying to get more than productivity gains from AI, this is the structural question worth asking: who in your organization has the vantage point and the mandate to find the right problems? Not implement AI. Find the problems worth solving with it.
In most organizations, that person doesn't exist. It's not a listed role. It's rarely in the vendor's scope. It's the gap that most AI projects fall into — not because the tools don't work, but because nobody was looking at the work from the right angle before the tools arrived. McKinsey's 2026 State of AI data puts a number on the result: 80% of companies report improved individual productivity. Six percent qualify as high performers with meaningful financial impact.
The tool-builder assumption explains most of the gap between those two numbers.
Evans doesn't say this means AI can't drive transformation. He says transformation follows the same institutional patterns it always has: slow, top-down, requiring formal decisions and 18-month implementation cycles for anything that spans departments and regulatory regimes. The democratization story is real for individuals. At the organizational level, it's harder and slower and requires someone doing the problem-discovery work that most employees aren't positioned to do.
That's the assumption worth examining before you roll out the tools.
If you want that question answered for your specific situation, the Forge Playbook does it. Answer a few questions about your business and we'll put together a tailored outline of which workflows are worth automating and what a realistic budget looks like for each. Free, no obligation, takes about three minutes.
